Previous research suggests that predictive mechanisms are essential in perceiving social interactions. However, these studies did not isolate (a priori expectations about how partners in an interaction react to one another) from (a posteriori processing of both partner's actions). This study investigated action prediction during social interactions while controlling for integration confounds. Twenty participants viewed 3D animations depicting an action-reaction interaction between two actors. At the start of each action-reaction interaction, one actor performs a social action. Immediately after, instead of presenting the other actor's reaction, a black screen covers the animation for a short time (occlusion duration) until a still frame depicting a precise moment of the reaction is shown (reaction frame). The moment shown in the reaction frame is either temporally aligned with the occlusion duration or deviates by 150 ms or 300 ms. Fifty percent of the action-reaction trials were semantically congruent, and the remaining were incongruent, e.g., one actor offers to shake hands, and the other reciprocally shakes their hand (congruent action-reaction) versus one actor offers to shake hands, and the other leans down (incongruent action-reaction). Participants made fast congruency judgments. We hypothesized that judging the congruency of action-reaction sequences is aided by temporal predictions. The findings supported this hypothesis; linear speed-accuracy scores showed that congruency judgments were facilitated by a temporally aligned occlusion duration, and reaction frames compared to 300 ms deviations, thus suggesting that observers internally simulate the temporal unfolding of an observed social interction. Furthermore, we explored the link between participants with higher autistic traits and their sensitivity to temporal deviations. Overall, the study offers new evidence of prediction mechanisms underpinning the perception of social interactions in isolation from action integration confounds.
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http://dx.doi.org/10.3390/brainsci12040432 | DOI Listing |
Int J Bipolar Disord
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Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt-Goethe University, Frankfurt am Main, Germany.
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December 2024
The Department of Mechanical Engineering and Mechatronics, Ariel University, Ariel, Israel.
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Clinical Nursing Teaching and Research Section, The Second XiangYa Hospital, Central South University, No139, Renmin Road, Changsha, 410011, China.
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Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
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December 2024
School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China.
Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences.
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